1 - Basics run

Requirements:

• Files linked via URL.

Objectives:

1. Read in necessary mass and energy data
2. Modify necessary values manually and programatically
3. Generate plots

# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent "ModuleNotFoundError" in later cells:
# !pip install git+https://github.com/NREL/PV_ICE.git@development

import PV_ICE
PV_ICE.__version__

from google.colab import files
files.upload()

import os
os.listdir()

pwd

testfolder = 'Tuesday'

if not os.path.exists(testfolder):
    os.makedirs(testfolder)

r1 = PV_ICE.Simulation(name='Sim1', path=testfolder); # Is it possible to define more than one simulation here?

r1.__dict__

r1.baselinepath = '/content'

r1.createScenario(name='standard', massmodulefile=r'baseline_modules_mass_US.csv', energymodulefile = 'baseline_modules_energy.csv' )

r1.scenario['standard'].addMaterial('glass', massmatfile='/content/baseline_material_mass_glass.csv', energymatfile='/content/baseline_material_energy_glass.csv')

r1.scenario['standard'].material['glass'].__dict__

r1.scenario['standard'].material['glass'].matdataIn_m

r1.calculateMassFlow()
r1.calculateEnergyFlow()

r1.calculateFlows()

r1.scenario['standard'].dataOut_m.keys()

Index(['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0',
       'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife',
       'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored',
       'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG',
       'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill',
       'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield',
       'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]',
       'Resold_Area', 'Resold_[W]', 'WeibullParams',
       'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure',
       'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad',
       'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL',
       'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure',
       'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad',
       'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL',
       'irradiance_stc'],
      dtype='object')

What can aggregate results from dataOut_m and matdataOut_m and compile the data so we can use it more easily

USyearly, UScum = r1.aggregateResults()
# r1.USyearly
# r1.UScum

USyearly.keys()

Index(['VirginStock_glass_Simulation1_standard_[Tonnes]',
       'VirginStock_silicon_Simulation1_standard_[Tonnes]',
       'VirginStock_Module_Simulation1_standard_[Tonnes]',
       'WasteAll_glass_Simulation1_standard_[Tonnes]',
       'WasteAll_silicon_Simulation1_standard_[Tonnes]',
       'WasteAll_Module_Simulation1_standard_[Tonnes]',
       'WasteEOL_glass_Simulation1_standard_[Tonnes]',
       'WasteEOL_silicon_Simulation1_standard_[Tonnes]',
       'WasteEOL_Module_Simulation1_standard_[Tonnes]',
       'WasteMFG_glass_Simulation1_standard_[Tonnes]',
       'WasteMFG_silicon_Simulation1_standard_[Tonnes]',
       'WasteMFG_Module_Simulation1_standard_[Tonnes]',
       'VirginStock_glass_Simulation1_decadence_[Tonnes]',
       'VirginStock_silicon_Simulation1_decadence_[Tonnes]',
       'VirginStock_Module_Simulation1_decadence_[Tonnes]',
       'WasteAll_glass_Simulation1_decadence_[Tonnes]',
       'WasteAll_silicon_Simulation1_decadence_[Tonnes]',
       'WasteAll_Module_Simulation1_decadence_[Tonnes]',
       'WasteEOL_glass_Simulation1_decadence_[Tonnes]',
       'WasteEOL_silicon_Simulation1_decadence_[Tonnes]',
       'WasteEOL_Module_Simulation1_decadence_[Tonnes]',
       'WasteMFG_glass_Simulation1_decadence_[Tonnes]',
       'WasteMFG_silicon_Simulation1_decadence_[Tonnes]',
       'WasteMFG_Module_Simulation1_decadence_[Tonnes]',
       'newInstalledCapacity_Simulation1_standard_[MW]',
       'newInstalledCapacity_Simulation1_decadence_[MW]',
       'ActiveCapacity_Simulation1_standard_[MW]',
       'DecommisionedCapacity_Simulation1_standard_[MW]',
       'ActiveCapacity_Simulation1_decadence_[MW]',
       'DecommisionedCapacity_Simulation1_decadence_[MW]'],
      dtype='object')

UScum.keys()

Index(['VirginStock_glass_Simulation1_standard_[Tonnes]',
       'VirginStock_silicon_Simulation1_standard_[Tonnes]',
       'VirginStock_Module_Simulation1_standard_[Tonnes]',
       'WasteAll_glass_Simulation1_standard_[Tonnes]',
       'WasteAll_silicon_Simulation1_standard_[Tonnes]',
       'WasteAll_Module_Simulation1_standard_[Tonnes]',
       'WasteEOL_glass_Simulation1_standard_[Tonnes]',
       'WasteEOL_silicon_Simulation1_standard_[Tonnes]',
       'WasteEOL_Module_Simulation1_standard_[Tonnes]',
       'WasteMFG_glass_Simulation1_standard_[Tonnes]',
       'WasteMFG_silicon_Simulation1_standard_[Tonnes]',
       'WasteMFG_Module_Simulation1_standard_[Tonnes]',
       'VirginStock_glass_Simulation1_decadence_[Tonnes]',
       'VirginStock_silicon_Simulation1_decadence_[Tonnes]',
       'VirginStock_Module_Simulation1_decadence_[Tonnes]',
       'WasteAll_glass_Simulation1_decadence_[Tonnes]',
       'WasteAll_silicon_Simulation1_decadence_[Tonnes]',
       'WasteAll_Module_Simulation1_decadence_[Tonnes]',
       'WasteEOL_glass_Simulation1_decadence_[Tonnes]',
       'WasteEOL_silicon_Simulation1_decadence_[Tonnes]',
       'WasteEOL_Module_Simulation1_decadence_[Tonnes]',
       'WasteMFG_glass_Simulation1_decadence_[Tonnes]',
       'WasteMFG_silicon_Simulation1_decadence_[Tonnes]',
       'WasteMFG_Module_Simulation1_decadence_[Tonnes]',
       'newInstalledCapacity_Simulation1_standard_[MW]',
       'newInstalledCapacity_Simulation1_decadence_[MW]'],
      dtype='object')

r1.saveSimulation()

:) Saved Input and Output Dataframes

Step 5: Use internal plotting functions to plot results

From this list, select the one that fits your study and select the type of plotting method. There are various plotting options:

• plotScenariosComparison
• plotMaterialComparisonAcrossScenarios
• plotMetricResults: You can select the following keyword options: 'VirginStock', 'WasteALL', 'WasteEOL', 'WasteMFG'
• plotMaterialResults
• plotInstalledCapacityResults

You can also view all the keywords you can use by calling the function without argumnets, or by printing the keys to the module data or the material data

r1.plotScenariosComparison()

Please pass a keyword:

 ** Scenario Data In Mass  ['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50', 'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff', 'mod_Repair', 'mod_MerchantTail', 'mod_EOL_collection_eff', 'mod_EOL_pg0_resell', 'mod_EOL_pg1_landfill', 'mod_EOL_pg2_stored', 'mod_EOL_pg3_reMFG', 'mod_EOL_pg4_recycled', 'mod_EOL_reMFG_yield', 'mod_EOL_sp_reMFG_recycle', 'mod_EOL_pb1_landfill', 'mod_EOL_pb2_stored', 'mod_EOL_pb3_reMFG', 'mod_EOL_pb4_recycled']

 ** Scenario Data Out Mass  ['Area', 'Cumulative_Active_Area', 'EOL_BadStatus', 'EOL_Landfill0', 'EOL_PATHS', 'EOL_PG', 'Effective_Capacity_[W]', 'Landfill_0_ProjLife', 'MerchantTail_Area', 'MerchantTail_[W]', 'ModuleTotal_MFG', 'P2_stored', 'P3_reMFG', 'P4_recycled', 'PB1_landfill', 'PB2_stored', 'PB3_reMFG', 'PB3_reMFG_unyield', 'PB3_reMFG_yield', 'PB4_recycled', 'PG1_landfill', 'PG2_stored', 'PG3_reMFG', 'PG3_reMFG_unyield', 'PG3_reMFG_yield', 'PG4_recycled', 'Power_Degraded_[W]', 'Repaired_Area', 'Repaired_[W]', 'Resold_Area', 'Resold_[W]', 'WeibullParams', 'Yearly_Sum_Area_EOLby_Degradation', 'Yearly_Sum_Area_EOLby_Failure', 'Yearly_Sum_Area_EOLby_ProjectLifetime', 'Yearly_Sum_Area_PathsBad', 'Yearly_Sum_Area_PathsGood', 'Yearly_Sum_Area_atEOL', 'Yearly_Sum_Power_EOLby_Degradation', 'Yearly_Sum_Power_EOLby_Failure', 'Yearly_Sum_Power_EOLby_ProjectLifetime', 'Yearly_Sum_Power_PathsBad', 'Yearly_Sum_Power_PathsGood', 'Yearly_Sum_Power_atEOL', 'irradiance_stc']

r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_virgin_eff')

r1.plotMaterialResults(keyword='VirginStock')

r1.plotMetricResults()

[]

r1.plotInstalledCapacityResults()

[]